Reconstructing dynamic molecular states from single-cell time series
Abstract
The notion of state for a system is prevalent in the quantitative sciences and refers to the minimal system summary sufficient to describe the time-evolution of the system in a self-consistent manner. It is a prerequisite for a principled understanding of the inner working of a system. Due to the complexity of intracellular processes experimental techniques that can retrieve such a sufficient summary are beyond reach. For the case of stochastic biomolecular reaction networks we show how to complete the partial state information accessible by experimental techniques into a full system state using mathematical analysis together with a computational model. This is intimately related to the notion of conditional Markov processes and we introduce the posterior master equation and derive novel approximation to the corresponding infinite-dimensional posterior moment dynamics. We exemplify this state reconstruction approach using both, in silico data and single-cell data from two gene expression systems in Saccharomyces cerevisiae, where we reconstruct the dynamic promoter and mRNA states from noisy protein abundance measurements.
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Origin | Files produced by the author(s) |
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Origin | Files produced by the author(s) |
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